Methods for Diagnosing Glaucoma Utilizing Combinations of FD-OCT Measurements from Three Anatomical Regions of the Eye

ABSTRACT

This invention discloses methods and systems for diagnosing glaucoma by combining diagnostic parameters derived from optical coherence tomography images of three different anatomic regions of the eye, including the macular ganglion cell complex (mGCC), the peripapillary nerve fiber layer (ppNFL), and the optic nerve head (ONH). The combined diagnostic parameters form a reduced set of global parameters, which are then fed to pre-trained machine classifiers as input to arrive at a single diagnostic indicator for glaucoma. Also disclosed are methods for training a machine classifier to be used in methods and systems of this invention.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.61/174,463, file on Apr. 30, 2009. The above application(s) is herebyincorporated herein by reference.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with government support under Contract No. RO1EY013516 awarded by the National Institute of Health. The government hascertain rights in the invention.

FIELD OF THE INVENTION

The present invention relates in general to glaucoma diagnosis. Morespecifically, the invention relates to methods and systems for combiningdiagnostic information derived from two or three anatomic regions of theeye to arrive at a single diagnostic indicator for glaucoma. The keycombinations of anatomic regions are the macular ganglion cell complex,the peripapillary nerve fiber layer, and the optic nerve head; or themacular ganglion cell complex and the peripapillary nerve fiber layer.

BACKGROUND OF THE INVENTION

Glaucoma is the second leading cause of blindness in the U.S. It ischaracterized by loss of retinal ganglion cells, thinning of the retinalnerve fiber layer, or cupping of the optic disc. Conventional tests arebased on measurements of intraocular pressure and visual field tests.Because there may be significant structural loss before functional lossbegin to manifest, these tests often miss early stages of the disease.In theory, diagnosis based on structural loss should be able to detectglaucoma at a stage earlier than detectable visual field defects.Structural damages associated with glaucoma are characteristicallydistributed in the above mentioned three anatomic regions, thus,diagnostic tools capable of providing information on structural changeson these anatomic regions are potentially useful for glaucoma diagnosis.

To this end, there are currently a number of diagnostic methods andtools capable of providing direct and indirect information on differentanatomic regions of the eye. In particular, the recent introduction ofFourier-domain optical coherence tomography (FD-OCT) enables highdensity retinal mapping over a large area in a short period of time. Theshort image acquisition time reduces motion error, and the high imagedensity and large image area permits more detailed pattern analysis.FIG. 1 shows an example of a FD-OCT image depicting the structural lossdue to glaucoma in the posterior segment of the eye.

Ironically, these technological advances have created an informationalcrisis. Given the variety of methods and instrumentations providing abewilderment of diagnostic information, how to interpret and combinethese diverse sources of information to arrive at a meaningful clinicaldiagnosis has become a major challenge. Moreover, while imagingtechnologies are now available to provide detailed images of the eye,qualitative interpretation of these images by trained experts can varywidely. Thus, the information has not led to better or easier clinicaldecision making.

Therefore, there exists an urgent need for automated, quantitativemethods of analyzing imaging data to arrive at reliable and reproduciblediagnosis of glaucoma.

SUMMARY OF THE INVENTION

In view of the above, it is an object of this invention to provide amethod for quantitatively and automatically analyzing imaging dataobtained from optical coherence tomography (OCT), particularly FD-OCT.

It is also an object of this invention to provide a general method forcombining diagnostic information from the three anatomic regionsaffected by glaucoma to arrive at a diagnosis.

It is yet another object of this invention to provide a diagnosticsystem for automatically diagnosing a patient's glaucoma status.

These and other objects of the invention are accomplished by the methodsand systems described herein.

While not intending to be bound by any particular theory, this inventionwas inspired by the inventors' observation that the damages associatedwith glaucoma generally include thinning of the inner retinal layers inthe macula region and the nerve fiber layer around the optic disc, aswell as enlarged cup and reduced rim in the optic nerve head. Theinventors observed that glaucoma cases follow 3 different patterns wherethe damage to the eye is either superior-dominant, inferior-dominant, orevenly distributed between the superior and inferior hemispheres. Basedon these observations, the inventors of this invention hypothesized thatin each individual eye, the same pattern of damages may apply to theoptic nerve head (ONH), the peripapillary nerve fiber layer (ppNFL), andmost likely the macular ganglion cell complex (mGCC) as well.Accordingly, the inventors have devised a novel approach of combiningdiagnostic parameters from the three anatomic regions by taking intoaccount the correlated patterns of damages in the three regions. Theinventors further incorporated machine learning technologies to createautomated methods and systems of this invention.

In one aspect, the present invention provides a machine classifier basedmethod of diagnosing glaucoma in a subject. Methods in accordance withthis aspect of the invention generally include the steps of obtainingOCT images from each of the three anatomic regions of the patient's eye;processing the images to obtain a predetermined collection of diagnosticparameters; transforming the collection of diagnostic parameters toarrived at a reduced set of global diagnostic parameters; and thenapplying a pre-trained machine classifier to the reduced set of globaldiagnostic parameters to arrive at a single diagnostic indicator. Thepredetermined collection of diagnostic parameters will have three basiccharacteristics. First, each diagnostic parameter in the collection isderived from OCT images of one of the three anatomic regions. Second, incases where the diagnostic parameter is a glaucoma patterncross-correlation (GPCC) parameter, it is further classified as beingone of the following three GPCC types: superior, inferior, or even.Third, at least mGCC and ppNFL, and all three GPCC types within eachregion must be represented in the collection.

In still a further aspect, the present invention provides a method oftraining a machine classifier for glaucoma diagnosis. Methods inaccordance with this aspect of the invention generally include the stepsof providing a training dataset consisting of an initial set ofdiagnostic parameters obtained from a sample population of subjects;selecting a trial set diagnostic parameters from the dataset;transforming the trial set of diagnostic parameters to arrive at areduced set of global diagnostic parameters, training the machineclassifier using with the reduced global diagnostic parameters; andoptimizing the machine classifier using area under the receivingoperator characteristic curve (AROC) as a guide. The sample ofpopulation consists of a first population of healthy subjects and asecond population of subjects suffering from perimetric glaucoma(glaucoma diagnosis confirmed by perimetry, also called visual field).The initial set of diagnostic parameters will have, the following twocharacteristics: First, each diagnostic parameter is derived from OCTimages of an anatomic region of a subject's eye selected from the groupconsisting of mGCC, ppNFL, and ONH. Second, in cases where thediagnostic parameter is a glaucoma pattern cross-correlation parameter,it is further classified as being one of three GPCC types selected fromsuperior, even, and inferior. The trial set of diagnostic parameters areselected such that at least mGCC and ppNFL and all three GPCC types foreach region are represented. The process may be repeated with selectionof different trial set of diagnostic parameters so that an optimal setof diagnostic parameters may be determined.

In yet another aspect, the present invention provides a system fordiagnosing glaucoma in a patient. Systems in accordance with this aspectof the invention generally include a computing unit configured forreceiving a predetermined collection of diagnostic parameters,transforming the collection of diagnostic parameters according to apredetermined formula to arrive at a reduced set of global parameters,and applying a pre-trained machine classifier to the reduced globalparameters to arrive at a single indicator; and an input/output unitoperatively connected to the computing unit for receiving input from anend user and outputting the indicator to the end user. The predeterminedcollection of diagnostic parameters has the following threecharacteristics: First, each diagnostic parameter is derived from OCTimages of an anatomic region of the patient's eye selected from thegroup consisting of mGCC, ppNFL, and ONH. Second, in cases where theparameter is a glaucoma pattern cross-correlation parameter, it isfurther classified as one of three GPCC types consisting of superior,inferior, and even. Third, at least mGCC and ppNFL regions, and all GPCCtypes of each region are represented in the collection of diagnosticparameters.

The computing unit may be the same computing unit operating the OCTdevice, a PC, or any other suitable computers known in the art. Methodsfor configuring the computing unit generally involve loading a softwareprogram implementing methods of this invention. Given the detaileddescription of methods disclosed herein, those skilled in the art willbe able to write computer programs implementing the methods. Exemplaryprogramming tools may include C/C++, Matlab, FORTRAN, or any otherprogramming language commonly known in the art. The computing unit maybe any suitable computing unit, including standalone PCs, mainframeservers, or a integrated computing unit on the OCT device, but are notlimited thereto.

In the above methods and systems, any OCT device capable of high speed,high density scanning may be used, but FD-OCT is preferred.

The diagnostic parameters may be any commonly used or future defineddiagnostic parameters based on OCT scans of a single anatomic region.Exemplary diagnostic parameters for the mGCC region may include superiorGPCC, superior hemispheric average, even GPCC, global average, inferiorGPCC, inferior hemispheric average, focal loss volume, global lossvolume and pattern coefficient variation. Exemplary diagnosticparameters for the ppNFL region may include superior GPCC, superiorhemispheric average, superior quadrant average, even GPCC, globalaverage, inferior GPCC, inferior hemispheric average, inferior quadrantaverage focal loss volume, global loss volume and pattern coefficientvariation. Exemplary diagnostic parameters for the ONH region mayinclude rim area or volume parameters (superior GPCC, superiorhemispheric average, superior quadrant average, even GPCC, globalaverage, inferior GPCC, inferior hemispheric average, inferior quadrantaverage focal loss volume, global loss volume) and cup parameters(vertical cup/disc ratio, horizontal cup/disc ratio, cup/disc arearatio, and cup/disc volume ratio).

The mathematics for transforming or combining the parameters are notparticularly limited, so long as parameters from all three anatomicregions contribute to the resulting transformed global parameter.Exemplary mathematics for transforming the diagnostic parameters mayinclude addition, subtraction, multiplication, or any other suitablemathematical functions known in the art.

The machine classifier is also not particularly limited. Any machineclassifier commonly known in the art may be suitably used. Exemplarymachine classifier may include linear discriminant function (LDF),logistic regression model, support vector machine (SVM) and relevancevector machine (RVM), but are not limited thereto. Preferably, SVM andRVM classifiers with Gaussian kernels are used.

Methods and systems of this invention will have at least the advantagesof being easy to administer and operate. Because OCT system arecurrently available in many clinics, methods and systems of thisinvention can be easily deployed as a software upgrade. The diagnosticindicator of the present invention, which incorporate information on thepattern of glaucomatous damage in 3 separate anatomic regions, hashigher glaucoma diagnostic sensitivity and specificity than parametersderived from any single diagnostic region. Reducing the myriad ofdiagnostic information into a single diagnostic indicator makes iteasier for the clinician to decide the level of treatment (medications,laser, or surgery) and the frequency of followup visits that are neededfor the eye being tested.

Other aspects and advantages of the invention will be apparent from thefollowing description and the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an FD-OCT image showing loss of retinal ganglion cells,thinning of the retinal nerve fiber layer, and cupping of the opticdisc.

FIG. 2 (A) shows a mGCC scan pattern consisting of 16 lines raster scanover 7×7 mm macula. (B) shows macula ganglion cell complex map based ona mGCC scan.

FIG. 3 (A) shows ONH, combined radial scans and circle scans covering a5 mm diameter region. (B) shows a ppNFL thickness map and cup/discsegmentation based on an ONH scan. (C) shows cup/disc/rim based on anONH scan.

FIG. 4 shows schematic representations of an exemplary strategies ofcombining diagnostic parameters. Step 1: combine parameters for threeanatomic regions; Step 2: combine inferior, superior and overallinformation; Step 3: combine different parameters.

FIG. 5 shows exemplary deviation maps of mGCC and ppFNL from an eye withan inferior pattern of glaucoma. (A) Thickness map, deviation map andfraction deviation map (B) Pattern deviation map and focal loss mask

FIG. 6 shows exemplary characteristic maps of three types of glaucoma.

FIG. 7 shows flow chart outlining the procedures for combiningdiagnostic parameters. The abbreviations Sup. and Inf. denote superiorand inferior while Max denotes maximum among three combined GPCCparameters. The other abbreviations are summarized in Table 1.

DETAILED DESCRIPTION

The present invention will now be described in detail by referring tospecific embodiments as illustrated in the accompanying figures.

DEFINITIONS

As used herein, the term mGCC refers to the macular area of the ganglioncell complex, which consists of the nerve fiber layer (NFL), ganglioncell layer (GCL) and the inner plexiform layer (IPL) of the retina.

As used herein, the term ppNFL refers to peripapillary Nerve FiberLayer.

As used herein, the term ONH refers to the optic nerve head.

As used herein, the term MLM refers to machine learning methods, ormachine classifiers. A machine classifier is a statistical procedure inwhich individual items are placed into groups based on quantitativeinformation on one or more characteristics inherent in the items(referred to as traits, variables, characters, etc) and based on atraining set of previously labeled items.

As used herein, the term GPCC refers to glaucoma patterncross-correlation. The formula for computing a pattern cross-correlationis discussed in detail below.

Overview

Many diagnostic parameters with high diagnostic power for glaucoma areavailable from images and maps of different anatomic regions. However,as mentioned above, practicing ophthalmologists do not know how tocombine the information from the parameters within the same anatomic oracross different anatomic regions to arrive at a meaningful diagnosisfor glaucoma. This state of affair may partially be explained by thefact that the exact underlying cause of glaucoma is still not wellunderstood, thus, the relationships between the different physiologicpresentations measured by the different diagnostic parameters are noteasily discoverable. Without knowing the relationships between thedifferent diagnostic parameters, it is difficult to fathom anycombinations of the parameters that may yield a better performingdiagnostic indicator than the uncombined parameters alone.

The present invention describes methods that overcome the aforementioneddifficulty. In particular, this invention provides a method forcombining parameters within and across anatomic regions.

For the three regions affected by glaucoma, OCT provides correspondingdiagnostic parameters in each region. For example, in RTVue FD-OCT(available from Optovue, Inc., Fremont, Calif., USA), the parameters aregenerated through two scan patterns. The mGCC thickness map isconstructed by mGCC scan (FIG. 2). The ppNFL thickness map and opticdisc shape are constructed by ONH scan (FIG. 3). Combining theparameters from multiple regions to yield a single indicator willprovide ophthalmologists a direct interpretation for glaucoma diagnosis.

As a proof of concept, a total of 157 eyes from 50 normal (N) and 50age-matched perimetric glaucoma (PG) participants in the AdvancedImaging for Glaucoma Study (AIGS, www.AIGStudy.net) were evaluatedaccording to methods of the invention. The age of participants rangesfrom 41 to 75 years old.

For glaucoma, loss of the mGCC in the superior macula corresponds tosuperior ppNFL damage and superior rim loss. Loss in the inferiorhemisphere works in similar ways. Thus, in Step 1, parameters acrossmGCC, ppNFL and the rim region may be combined first. Then, in Step 2,superior, inferior and overall parameter may be combined (FIG. 4). In analternate strategy, the order of combination may be reversed, i.e.,superior, inferior and overall parameter may be combined first for eachanatomy region followed by combining the parameters across the 3anatomic regions. In this way, steps 1 and 2 will result in a set ofreduced global parameters. Finally, in Step 3, a machine classifier isapplied to the reduced global parameters to arrive at a single indicatorfor glaucoma.

Machine Classifiers

Several machine classifiers such as linear discriminant function (LDF)(1), logistic regression model (2), support vector machine (SVM) (3) andrelevance vector machine (RVM) (4) have been applied in eye studies forcombining diagnostic parameters in discriminating N and PG eyes (5-9).In the LDF approach, diagnostic parameters are combined through a linearcombination equation with different weight coefficients summed into onenumber. The weights are computed so that the LDF is optimized fordiscriminating two classes (N and PG in this invention). A logisticregression model assumes a logit link between probability of being inone class (PG in this invention) and diagnostic parameters. The modelgenerates a linear equation between the logit (P), log(p/1−p), and thediagnostic parameters with different coefficients, where the p denotethe probability of glaucoma and the coefficients are estimated inoptimizing the log-likelihood of the underlying model. The SVM approachsearches a hyperplane that optimizes the margin between classes (N andPG in this invention) in classification analysis. The relationshipbetween classes can be linear, or nonlinear through a kernel function(Gaussian, polynomial, wavelet, and so on).

Briefly, in SVM, the machine classifier is first subjected to a trainingprocess, and then followed by a testing process. In the trainingprocess, SVM uses the training dataset to find supporting vectors thatoptimize the separation between N and PG groups. In the testing process,those supporting vectors are applied to the testing dataset and generatecomposite scores for each observation in the test dataset.

The RVM approach is basically an extension of SVM. It generalizes thesupport vector methodology with a Bayesian approach adopted duringoptimizing the margin between classes and searches relevance vectors inthe training process. The RVM generated composite scores can be furthertransformed into a zero to one probability score. In some embodiments ofthe invention, this is the probability for glaucoma.

The scores could provide ophthalmologists a more intuitiveinterpretation for glaucoma diagnosis. In the exemplary embodimentsdescribed herein, the inventors demonstrated two machine learningmethods—SVM and RVM—to combine diagnostic parameters in Step 3. Thesetwo machine classifiers are preferred because both classifiers allowmore flexibility for the relationship between N and PG groups than theother classifiers.

Diagnostic Parameters

The inventors have previous developed methods of analyzing the patternof mGCC thickness loss and defined the diagnostics parameters focal lossvolume (FLV), global loss volume (GLV), pattern coefficient variation(PCV) from mGCC map. Details of these diagnostic parameters have beensubmitted for publication (10) and is the subject of the inventors'copending application Ser. No. 12/139,375, the entire content of whichare incorporated herein by reference.

In one exemplary embodiment, maps of mGCC thickness loss—the deviation(D) map, the fractional deviation (FD) map and the pattern deviation(PD) map—were computed. First the mGCC maps of all normal eyes wereaveraged, point by point, to create a normal reference map. The D map isthe thickness map under consideration minus the normal reference map.The FD map is the thickness map under consideration minus the normalreference map divided by the normal reference map. The pattern map isthe mGCC thickness map normalized (divided) by its own overall average.The PD map is the pattern map under consideration minus the normalreference pattern. The FD map shows the percentage of mGCC loss. The PDmap shows how the mGCC thickness pattern differs from normal. Thesedeviation map calculation can be applied to other thickness map, such asNFL map or total retinal thickness map (FIG. 5A-B).

Three pattern-based diagnostic parameters were then computed from thetwo derivative maps. The FLV is the summation of FD in the region wherethere is significant focal loss. Significant focal loss mask is definedas FD more than 1.65 standard deviation (SD) below the normal average(below the fifth percentile of normal distribution) (FIG. 5). The GLV isthe sum of FD in areas where FD is negative. The PCV is the root meansquare of the PD map.

For ppNFL map, same method can be applied. However, FLV and GLVcalculation on D map were preferred over fraction deviation map. Ourexperience showed that this provided higher diagnostic accuracy.

Glaucoma relatively spares the centrocecal area and has variablesuperior or inferior dominance (usually inferior). Pattern matchingusing cross-correlation is preferably used to distinguish between thesetypes of glaucoma and normal. The analysis could be performed using D,FD or PD maps.

Preferably the PD map is used. The training dataset contain normal andglaucomatous eyes. The glaucomatous eyes are divided into the following3 disease categories based on the pattern of loss of mGCC or ppNFL. Thisdivision could also be applied to ONH rim area or volume. Thecharacteristic maps for each category of glaucoma is then compiled byaveraging the maps of eyes within each category (FIG. 6).

As used herein, the terms Inferior glaucoma, Even glaucoma, and Superiorglaucoma are defined as follows:

1. Inferior glaucoma (IG): average map of perimetric glaucoma eye withSID value>mean+1 SD of normal.

2. Even glaucoma (EG): average map of perimetric glaucoma eye with SIDvalue within ±1 SD of normal.

3. Superior glaucoma (SG): average map of PG eye with SID value<mean−1SD of normal.

The pattern cross-correlation (PCC) value is computed bycross-correlation of the map under testing with the characteristic mapsof any type of glaucoma from one of the maps, such as fraction deviationmap, pattern deviation map and deviation map. For example, FD-PCC can bedefined as:

PCC=∫_(A)(FD*CFD)/[(∫_(A)FD*FDdxdy)^(1/2)(∫_(A)CFD*CFDdxdy)^(1/2)]

where A is the area of the map, FD is the fraction deviation of the eyebeing tested, CFD is the characteristic pattern deviation of the diseaseunder consideration, x is the horizontal dimension of the map, and y isthe vertical dimension of the map.

Using PD and D map, similar parameter called PD-PCC and D-PCC may becreated. For mGCC map, it is preferable to use FD map to calculate thePCC for glaucoma analysis. For ppNFL map, it is preferable to use D mapto calculate the PCC for glaucoma analysis.

As there are three types of glaucoma, three PCC parameters are definedon mGCC map and other three PCC parameters are defined on ppNFL map.Accordingly, as used herein, the diagnostic parameters regarding PCC aredenoted by GPCC for glaucoma analysis.

An Exemplary Combination of Parameters

A total of 21 parameters from the three maps were considered forcombined parameters. They are FLV, GLV, PCV, overall average, inferiorand superior thicknesses, inferior, superior and even GPCC diagnosticparameters from both mGCC and ppNFL maps, as well as cup/disc ratio, rimarea and rim volume parameters from rim map.

It is important to know that a diagnostic power has no monotone trendwith the number of predictors in a MLM. Thus, a subset of diagnosticparameters is searched to be the predictors used in MLM.

The inclusion or exclusion of a parameter is in general based onsearching the best subset of parameters or forward-backward selectionguided by optimizing the diagnostic power. The first method requires2,097,151 (2²¹−1) steps and the second method requires at least 21 stepsfor 21 diagnostic parameters. Both methods are computational expensiveand might limit memory resources in computational software so that acomputationally efficient approach is desirable. Also, neither methodcould guarantee that the remaining parameters are across three regions.For example, a previous eye study (9) had formulated a combinedparameter from retinal nerve fiber layer (RNFL), ONH, and macularthickness scans based on time domain OCT. In this study, the selectionfor the best parameters to be combined was based on principle componentanalysis and followed by searching the best subset of parameters. Thefinalized combined parameters from the best subset were only across RNFLand ONH regions. Another eye study (8) had evaluated the bestcorrelation between diagnostic parameters and visual field meandeviation (in general denoted by MD) to select 8 out of 38 parametersfrom the above three scans in time domain OCT. Once again, the selected8 parameters were only across RNFL and ONH regions. None of the priorart methods were able to incorporate information from all three anatomicregions into their final diagnostic indicator. In contract, thisinvention provides an explicit 3-Step approach to combine parametersfrom the information across all three regions.

In one exemplary embodiment of this invention, the strategy as shown inFIG. 4 was used. The details and validation results are as follows:

Step 1: Combine Parameters Across mGCC, ppNFL and Rim Region

The parallel parameters were combined additively for FLV, GLV, PCV andthree GPCC parameters (inferior, superior, and even) across regions.

Although in this embodiment, parameters from all three anatomic regionsare used, in other embodiments, combinations that combine only mGCC andppNFL parameters are also workable.

Step 2: Combine Inferior, Superior and Overall Information.

A global variable from the three combined GPCC parameters was defined toindicate the worst structural loss reflected by GPCC parameters (maximumamong the three combined GPCC parameters). The diagnostic power wasevaluated by the area under receiving operative characteristic curve(AROC). Table 1 summarizes the AROC values for each single and acrossregions parameters. The analysis was based on 50 normal and age-matchedPG participants from AIGS study.

TABLE 1 Diagnostic Power Analysis for Single Fourier-Domain OpticCoherence Tomography Parameters. Analysis Parameter AROC Standard ErrormGCC mGCC FLV 0.920 0.026 mGCCGLV 0.917 0.025 mGCC superior GPCC 0.8640.032 mGCC inferior GPCC 0.881 0.029 mGCC even GPCC 0.880 0.030 mGCCPCV0.894 0.027 mGCC thickness AVG 0.900 0.028 mGCC thickness inferior 0.8740.031 mGCC thickness superior 0.884 0.029 ppNFL ppNFLFLV 0.924 0.022ppNFL GLV 0.908 0.027 ppNFL even GPCC 0.901 0.028 ppNFL inferior GPCC0.891 0.029 ppNFL superior GPCC 0.888 0.031 ppNFLPCV 0.862 0.032ppNFLTAVG 0.907 0.029 ppNFLT inferior 0.878 0.031 ppNFLT superior 0.8850.032 ONH cup/disk area ratio 0.862 0.037 rim area 0.875 0.034 rimvolume 0.886 0.033 Across Regions FLV 0.944 0.018 GLV 0.932 0.022 PCV0.898 0.026 GPCC 0.918 0.024

It is clear from the table that the reduced set of global parameters(the Across Regions parameters) have higher diagnostic power than theircomponent single-region parameters. For example, among the single-regionparameters, the FLV parameters showed the highest AROC at around 0.92.After combination, the resulting combined FLV showed a 0.02 increased inAROC at about 0.94. Other parameters showed even greater improvement.

In effect, steps 1 and 2 of the method reduced an initial set of 21trial parameters to 6 reduced global parameters which made efficient useof the information provided by the parameters of all three anatomicregions. Based on the results of Steps 1 and 2, four across-regionparameters (FLV, GLV, PCV and GPCC), plus cup/disk area ratio and rimvolume (higher AROC than rim area) were selected as the input for theMLM in Step 3.

Step 3: Machine Learning Method

Gaussian SVM and Gaussian RVM were used in this invention, where theGaussian kernels allowed for non linear relationship between N and PGgroups. The Gaussian kernel was also used in other studies todiscriminate N from glaucomatous eyes (6, 12, 13).

Cross Validation and AROC Optimization

Cross validation and grid search may be used to search the unknownparameters that underline the MLM models. In the Gaussian kernelfunction, the unknown parameter is the scale parameter (σ²); in SVM, itis the penalty parameter for misclassification (C), and only σ² in RVM.In exemplary embodiments of this invention, those unknown parameterswere estimated in optimization during the training process guided byAROC.

The process of MLM requires independent samples for training and testingdatasets. To efficiently use a dataset, cross validation was used tocreate training and testing dataset crossly. All participants wererandomly divided into k sets. Each of k sets was tested while the otherk−1 sets were used as a training dataset to create supporting vectorsfor SVM and relevance vectors for RVM. Hence, the test and trainingdataset were independent samples and the training dataset remained a bigportion (k−1)/k of a whole sample used in optimization to discriminate Nfrom PG eyes. The grid search was used to search the parameters (σ², C,k) optimizing the AROC generated by SVM scores and RVM scoresrespectively.

FIG. 7 is a flow chart illustrating in detail the three-steps process ofcombining parameters. The first four columns illustrate combiningparameters based on Steps 1 and 2 as described above. The last columnillustrates feeding the best subset of parameters to MLM to arrive at asingle diagnostic indicator as described in Step 3 above.

Evaluation of Diagnostic Power

Sensitivity and specificity at 1% threshold, in addition to AROC, wereused as benchmarks to further evaluate the diagnostic power of the SVM-and RVM-generated final composite scores along with the best singlediagnostic parameters from each anatomic region. The best singleparameters are mGCC FLV, ppNFL FLV, rim volume and cup/disk area ratio.

The mGCC and ppNFL overall average thickness were also evaluated sincethe average thickness parameter is in common used for glaucoma diagnosisin clinics. Table 2 summarizes the results of the diagnostic powerevaluation. With the 3-Steps approach of this invention, both SVM- andRVM-generated parameters have the best power to discriminate N from PGeyes. For example, compare to the single-region parameter with the bestAROC value, the method of this invention improved the AROC value from0.92 to about 0.96 and enhanced the sensitivity from 0.57 to about 0.78.

Possibly because of the small study sample, the improvement of AROC wasmarginally significant (p≦0.10) compared to mGCC FLV. However, theimprovement over the other parameters were (highly) statisticallysignificant. In clinical settings, the threshold of sensitivity at 1%threshold would be more practical than the AROC values. The improvementin sensitivity is dramatic (p<0.005) and the increment is more than 35%.

These results demonstrate that methods of this invention, which is basedon the concept of combining diagnostic parameters from three anatomicregions, is able to significantly increase the diagnostic power indiscriminating N from PG eyes.

TABLE 2 Diagnostic Power Analyses for Best Fourier-Domain Fourier-Domain Optic Coherence Tomography Parameters OCT AROC SensitivitySpecificity parameters (SE) P1 P2 (SE) P1 P2 (SE) Threshold SVM on ^(a)0.963 — 0.40 0.79 — 0.32 1.00 0.20 (0.014) (0.05) (0.00) RVM on ^(a)0.960 0.40 — 0.78 0.32 — 1.00 0.83 (0.014) (0.05) (0.00) ppNFL FLV (%)0.924 0.02 0.03 0.46 <0.0001 0.0001 1.00 15.58 (0.022) (0.07) (0.00)mGCC FLV (%) 0.920 0.09 0.10 0.57 0.0019 0.003 0.99 5.39 (0.026) (0.07)(0.01) ppNFLT AVG 0.907 0.01 0.02 0.57 0.0012 0.002 1.00 75.22 (μm)(0.029) (0.07) (0.00) mGCCT AVG 0.900 0.001 0.01 0.46 <0.0001 0.00010.99 81.10 (μm) (0.028) (0.07) (0.01) Rim volume 0.886 0.002 0.003 0.40<0.0001 <0.0001 0.96 0.081 (mm³) (0.033) (0.07) (0.03) Cup/disk 0.8620.001 0.002 0.29 <0.0001 <0.0001 0.96 0.74 area ratio (0.037) (0.07)(0.03) Abbreviations: SVM = supporting vector machine; RVM = relevancevector machine; P1 = p-value compared to SVM; P2 = p-value compared toRVM. The other abbreviations are same as in Table 1. ^(a) The parametersare based on combined parameters: FLV, GLV, PCV, and GPCC, and singleparameters: cup to disk ratio and rim volume. The differences amongspecificities are not statistically significant.

Alternative Embodiments

In one alternative embodiment, the superior/inferior/even GPCC for therim may be calculated in the same way as demonstrated above for mGCC andppNFL. This will allow the rim parameters to be combined with otherparameters in Step 1.

In another alternative embodiment, a maximum GPCC from the threepatterns for each region may be calculated first. Then the GPCC for thethree regions may be combined by addition. For other parameters, as theyonly provide the overall value, the flowchart does not need to bechanged.

In another alternative embodiment, the superior/inferior/evencombination may also be advantageously applied to other parameters. Forexample, the superior quadrant/inferior quadrant/overall average valuescan be computed first for ppNFL thickness. The resulting values can thenbe normalized according to a normal reference. Similarly, normalizedsuperior hemisphere/inferior hemisphere/overall average of mGCCthickness and normalized superior quadrant/inferior quadrant/overallaverage of disk rim can also be calculated. Then, the normalizedsuperior parameters can be combined in Step 1 by addition. Thiscombination will result in a reduced GPCC for the superior pattern. Inthe same way, the reduced GPCC for inferior and overall pattern can alsobe computed. Finally, the reduced GPCC from the three regions may becombined in Step 2 by finding their minimum. For FLV and GLV,superior/inferior/even parameter may be obtained by limiting theintegration area to superior/inferior/overall region. Parallelparameters from each of the three regions can be combined in Step 1 byaddition. Then the superior/inferior/even GPCC may be combined byfinding their minimum. For PCV, parallel parameters from three regionsmay be combined in Step 1 by addition. Then, the reduced PCV parametersmay be combined in Step 2 by finding their maximum. After all theinitial parameters are properly reduced to a set of reduced globalparameters in Step 2, they can be fed to an MLM in Step 3. Here, themachine classifier is not particularly limited. Exemplary machineclassifiers may include LDF, logistic regression model, SVM and RVM, butare not limited thereto. Preferably, Gaussian SVM and Gaussian RVM areused because Gaussian kernels allowed for non-linear relationshipbetween N and PG groups.

In another alternative embodiment, combination of superior/inferior/evenparameters can be applied first before combination of the 3 regions. ForppNFL thickness, the superior quadrant/inferior quadrant/overall averagecan be computed and then normalized according to a normal reference. Inthe same way, normalized superior hemisphere/inferior hemisphere/overallaverage of mGCC thickness, normalized superior quadrant/inferiorquadrant/overall average of disk rim can also be computed first. Then,the normalized ppNFL parameters can be combined by finding their minimumin Step 1. This combination yields a reduced GPCC parameter for ppNFL.In the same way, reduced GPCC parameters for mGCC and rim may also beobtained. Finally, the three reduced GPCC parameters may be combined byaddition in Step 2 to yield a reduced global GPCC parameter. For FLV andGLV, superior/inferior/overall parameter may be computed by limiting theintegration area to superior/inferior/overall region. The patternparameters for each region may be combined in Step 1 by finding theirminimum. Then, the reduced ppNFL/mGCC/rim parameters may be combined byaddition. For PCV, the three pattern parameters for the same region maybe combined in Step 1 by finding their maximum. Then, they may becombine across the region in Step 2 by addition. After the initial setof parameters were reduced to a reduced set of global parametersincorporating information from all three anatomic regions, they can beprovided as inputs to MLM in Step 3 to arrive at a single diagnosticindicator. As in above, the MLM can be any suitable machine classifierknown in the art, including LDF, logistic regression model, SVM and RVM,but are not limited thereto. Preferably, Gaussian SVM and Gaussian RVMare used because Gaussian kernels allowed for non linear relationshipbetween N and PG groups.

EXAMPLE Three Steps to Combine FD-OCT Parameters from Three AnatomicRegions

Step 1: Combine Parallel Parameters Across mGCC, ppNFL and Rim Regions

To generate “across region” parameters, a simple method that combinesparallel parameters for FLV, GLV, GPCC and PCV across mGCC and ppNFLregions by a weighted sum was considered. This method wascomputationally inexpensive. The weight was one for each region. It wasevaluated by AROC analysis such that the “across region” parameters werecombined additively with weight one if no statistical difference betweenthe AROC values of parallel parameters. The weights were proportional tothe AROC's and were summed up to two as the statistically difference ofAROC values was observed.

Step 2: Combine Inferior, Superior and Overall Region Information

A global variable from the three across-region GPCC parameters wasdefined to indicate the worst structural loss reflected by GPCCparameters (maximum among the three GPCC parameters) and denoted byglobal GPCC. The final set of input parameters used in MLM would be 4across-region parameters (FLV, GLV, global GPCC and PCV) and twoparameters from the rim region; cup/disk area ratio and a rim parameter.

It is important to know that the selection of input parameters wouldinfluence the performance of MLM in classification analysis. Rather thanusing a computationally intense process to select the input parameters,this invention uses the first two steps to combine the initialparameters across anatomic regions and reduce the parameters from 15initial parameters to 6 reduced global parameters to be used as input inMLM.

Moreover, the pattern concordance analysis between mGCC and ppNFLanalysis for GPCC parameter was conducted to validate the hypothesisthat all three anatomic regions would show analogous patterns ofstructural damages. The dominant area with the worst structural lossreflected by GPCC parameters was indicated by the GPCC parameter withthe maximum value and the distribution of the worst dominant area wasdescribed for each group in each region. Thus, the glaucomatous damagecan be categorized into three sets for inferior-dominant,superior-dominant, and even-dominant damage. The Kappa estimates werecalculated to quantify the agreement between mGCC and ppNFL pattern. Thecomparisons between Kappa estimates were based on a two-tailed Z test.

Step 3: Use MLM to Generate a Single Indicator of Glaucoma Status

To classify the reduced diagnostic parameters into a single diagnosticindicator, two types of MLM—SVM and RVM—were carried out. The SVMapproach searches a hyperplane that optimizes the margin between classes(N and PG in this study) in classification analysis. The relationshipbetween classes can be linear, or nonlinear through a kernel function(Gaussian, polynomial, wavelet, and so on). In brief, a training processis followed by a testing process in SVM. In the training process, SVMuses the training dataset to find supporting vectors that optimize theseparation between N and PG groups. Once the SVM is trained, thosesupporting vectors are applied to the test dataset and generatepredictive scores for each observation in the test dataset.

The RVM approach is basically an extension of SVM. It generalizes themethodology with a Bayesian approach adopted during optimizing themargin between classes and searches relevance vectors in the trainingprocess. The RVM-generated composite scores can be further transformedinto a zero-to-one probability score, that is, the probability forglaucoma in a test subject. The scores could provide ophthalmologists amore intuitive interpretation for glaucoma diagnosis. Especially, theBayesian machine learning classifier was also used in the HRT3 scanninglaser tomography (SLT) machine to generate the glaucoma probabilityscore (GPS), thus, the concept of RVM-generated probability score isalready familiar and accepted by clinicians.

Gaussian SVM and RVM were used in this study.

Sensitivity and Specificity Analyses

Sensitivity and specificity at 5% and 1% thresholds were used to furtherevaluate the diagnostic power of the final generated SVM and RVMpredicted scores beyond the AROC analysis. The parametric distributionof a diagnostic parameter was verified by visualizing a histogram aswell as Kolmogorov-Smirnov test. Nonparametric distribution was used ifnone of parametric distributions could be fitted. The thresholds of aGaussian distributed parameter can be formulated asmean_(N)+Z_(σ)SD_(N), where mean_(N) and SD_(N) were the mean andstandard deviation of the normal group, Z_(σ)−1.65 for the 5% cutoff,and Z_(σ)=−2.33 for the 1% cutoff. The parametric distribution of anon-Gaussian parameter or non parametric distribution were estimatedbased on 10,000 replicates, in which one eye was randomly selected fromeach participant. Both eyes of each participant were analyzed. To handlethe inter-eye correlation, a generalized estimating equation (GEE)approach was incorporate in the t-tests to compare clinical informationmean difference between groups while the Bootstrap p values were used inthe Kappa analysis. The inter-eye correlation was also appropriatedhandled in AROC, sensitivity and specificity analyses with formuladerived for clustered samples. The formula have been widely used inprevious AIGS studies. For carrying out MLM, one eye from eachparticipants was randomly selected to build up the learning models inthe training process since the models for MLM do not handle theinter-eye correlation while both eyes were used in the testing processto evaluate the prediction power based on AROC analysis with theinter-eye correlation appropriately handled.

The level of significance was set at P<0.05. The analyses were done inSAS 9.1 and in MATLAB 7.0. Exemplary MATLAB codes were freely availablefrom Prof Alain Rakotomamonjy at the University of Rouen for SVM, andfrom Mike Tipping's personal web-site for RVM.

Although the present invention has been described in terms of specificexemplary embodiments and examples, it will be appreciated that theembodiments disclosed herein are for illustrative purposes only andvarious modifications and alterations might be made by those skilled inthe art without departing from the spirit and scope of the invention asset forth in the following claims.

1. A machine classifier based method of diagnosing glaucoma in asubject, comprising: obtaining OCT images from three anatomic regions ofthe subject's eye, wherein said three anatomic regions consists of themGCC, the ppNFL, and the ONH; processing the images to obtain apredetermined collection of diagnostic parameters, wherein saidcollection of diagnostic parameters comprises the followingcharacteristics: a. each parameter is classified as belonging to one ofthe three anatomic regions, b. in cases where the parameter is a patterncross-correlation parameter, it is further classified as one of a GPCCtype selected from superior, inferior, and even, and c. at least themGCC and the ppNFL regions are represented in the collection ofdiagnostic parameters; and transforming the collection of diagnosticparameters by combining them across anatomic regions and GPCC typesaccording to a predetermined combination formula to arrive at a set ofreduced global parameters; and applying a pre-trained machine classifierto the reduced global parameters to arrive at a single indicator for thesubject's glaucoma status.
 2. The method of claim 1, wherein saidpredetermined collection of diagnostic parameters include parameters ofall three types of GPCC for each anatomic region.
 3. The method of claim1, wherein said predetermined collection of diagnostic parametersfurther include parameters for the ONH region.
 4. The method of claim 1,wherein said predetermined combination formula comprises transformingthe diagnostic parameters into six reduced global parameters consistingof a global GPCC, a combined-region FLV, a combined-region GLV, acombined-region PCV, a cup/disk, and a rim parameter.
 5. The method ofclaim 1, wherein said machine classifier is one selected from the groupconsisting of LDF, logistic regression, SVM, and RVM.
 6. The method ofclaim 1, wherein said combination formula comprises the steps of:combining diagnostic parameters of different anatomic regions; followedby combining diagnostic parameters of different GPCC types.
 7. Themethod of claim 1, wherein said combination formula comprises the stepsof: combining diagnostic parameters of different GPCC types within eachanatomic region; followed by combining diagnostic parameters ofdifferent anatomic regions.
 8. The method of claim 1 further comprisinga step of adjusting the indicator to account for the patient's age. 9.The method of claim 1, wherein said OCT images are obtained by an FD-OCTdevice.
 10. A method of training a machine classifier for glaucomadiagnosis, comprising: providing a training dataset consisting of aninitial set of diagnostic parameters obtained from a sample populationof subjects, wherein said subjects consists of a first population ofhealthy patient and a second population of patients suffering fromperimetric glaucoma, and said initial set of diagnostic parameters hasthe following characteristics: a. each diagnostic parameter is derivedfrom OCT images of an anatomic region of a subject's eye selected fromthe group consisting of mGCC, ppNFL, and ONH, and b. in cases where thediagnostic parameter is a pattern cross-correlation parameter, it isfurther classified as being one of three GPCC types selected fromsuperior, inferior, and even; selecting a trial set of diagnosticparameters such that at least the mGCC and the ppNFL regions arerepresented; transforming the trial set of diagnostic parameters bycombining them across regions and GPCC types to arrive at a set ofreduced global parameters; training the machine classifier todistinguish between healthy and glaucomatous using the reduced globalparameters as inputs; and optimizing the machine classifier using AROCas a guide.
 11. The method of claim 10, wherein said trail set ofdiagnostic parameters include parameters of all three GPCC types foreach anatomic region.
 12. The method of claim 10, wherein said trial setof diagnostic parameters further include parameters for the ONH region.13. The method of claim 10, wherein said transforming step comprises thesteps of: combining the diagnostic parameters of different anatomicregions; followed by combining diagnostic parameters of different GPCCtypes.
 14. The method of claim 10, wherein said transforming stepcomprises the steps of: combining diagnostic parameters of differentGPCC types within each anatomic region; followed by combining diagnosticparameters of different anatomic regions.
 15. The method of claim 10,wherein said machine classifier is one selected from the groupconsisting of LDF, logistic regression, SVM, and RVM.
 16. The method ofclaim 10, wherein said OCT images are obtained by a FD-OCT device.
 17. Asystem for diagnosing glaucoma in a patient, comprising: a computingunit configured for receiving a predetermined collection of diagnosticparameters, transforming the collection of diagnostic parametersaccording to a predetermined formula to arrive at a reduced set ofglobal parameters, and applying a pre-trained machine classifier to thereduced global parameters to arrive at a single indicator; and aninput/output unit operatively connected to the computing unit forreceiving input from an end user and outputting the indicator to the enduser, wherein said collection of diagnostic parameters has the followingcharacteristics: a. each diagnostic parameter is derived from OCT imagesof an anatomic region of the patient's eye selected from the groupconsisting of the mGCC, the ppNFL, and the ONH, b. in cases where theparameter is a pattern cross-correlation parameter, it is furtherclassified as one of three GPCC types consisting of superior, inferior,and even; and c. at least the mGCC and the ppNFL regions are representedin the collection of diagnostic parameters.
 18. The system of claim 17,wherein said collection of diagnostic parameters include all three typesof GPCC for each anatomic region.
 19. The system of claim 17, whereinsaid collection of diagnostic parameters further include parameters forthe ONH region.
 20. The system of claim 17, further comprising a OCTdevice for obtaining tomographic images of the patient's eye.
 21. Thesystem of claim 20, wherein said OCT device is a FD-OCT.